Modern agriculture faces persistent challenges in pest management:
- Inefficiency of traditional pest detection methods, which are slow and error-prone.
- Heavy reliance on chemical pesticides, leading to environmental hazards and health risks.
- Economic constraints for farmers due to recurring pesticide costs.
- High crop losses from uncontrolled pest outbreaks.
These challenges demand a sustainable, accessible, and scalable solution.
- Food Security: Reducing pest-related crop damage increases yields.
- Environmental Safety: Reducing pesticide use protects soil, water, and biodiversity.
- Farmer Empowerment: Mobile-based solutions bring advanced tools to smallholder farmers.
- Sustainability: Promotes organic farming and long-term ecological balance.
A mobile-powered pest detection and control system combining:
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Image-Based Pest Identification
- Farmers capture pest images using a smartphone application.
- Computer vision (TensorFlow Lite / ML Kit) identifies pest species in real time.
- Preprocessing methods (e.g., contrast enhancement, denoising) improve detection accuracy.
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Vibrational Signal Disruption
- Research-driven identification of communication frequencies for pests.
- Smartphone hardware emits targeted interference signals.
- Disrupts pest communication and feeding behaviors without chemical intervention.
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Farmer-Friendly Mobile Application
- Intuitive workflow: Capture → Detect → Frequency Selection → Emit.
- Provides actionable insights and eco-friendly recommendations.
- Data Collection: Images of pest species and communication signal datasets.
- Model Training: Lightweight CNN models optimized for mobile deployment.
- Signal Analysis: Frequency identification using Fourier analysis and testing.
- Prototype Development: Integration of detection and disruption modules in a mobile app.
- Evaluation: Simulation-based performance testing of pest communication disruption.
- Image recognition enables near-instant pest identification compared to manual scouting.
- Frequency-based disruption offers an eco-friendly alternative to pesticides.
- Mobile-first design ensures affordability and scalability for diverse farming contexts.
- Framework can be extended to support multiple pest types and regional languages.
- Reduced pesticide usage, improving soil and food quality.
- Lower farming costs, decreasing dependence on chemical solutions.
- Accessible technology, usable on affordable smartphones.
- Long-term sustainability, aligning with global goals for climate-smart agriculture.
- Computer Vision: TensorFlow Lite, ML Kit
- Signal Processing: FFT-based analysis for frequency identification
- Mobile Development: Android SDK (prototype)
- Data Handling: Python for preprocessing and model training